Joscha Bach

Authored papers (2)

• cimcWhitepaper

  Consciousness is a coherence-maximizing pattern implemented through self-organized second-order perception in self-organizing substrates — this is the core claim of the Machine Consciousness Hypothesis (MCH) advanced by the California Institute for Machine Consciousness (CIMC) in their April 2026 founding whitepaper. The MCH holds that general computational machines with sufficient resources possess the necessary and sufficient means to implement consciousness, and that the relevant test is interpretive validation of internal structure rather than behavioral output — a direct response to the demonstrated insufficiency of linguistic behavioral tests given that current LLMs produce conscious-sounding outputs through pattern-matching without evidence of the underlying functional organization. The framework adopts computational functionalism, combining Wolfram's (2002) Principle of Computational Equivalence with epistemological computationalism (Bach and Verdicchio, 2012), and situates consciousness as the simplest learning algorithm discoverable by evolutionary search to bootstrap coherent agency in a self-organizing substrate whose architecture is not pre-specified. CIMC's operational definition — a system is conscious if it implements self-organized second-order perception that increases global coherence — generates testable predictions about developmental trajectories, functional signatures, and phase transitions, to be evaluated through Request Confirmation Networks, Neural Cellular Automata experiments, and interpretability of representational embedding spaces. The paper argues this implies that consciousness research requires philosophy and construction to discipline each other, that behavioral testing of existing systems is insufficient as the primary methodology, and that the field's current fragmentation across neuroscience, philosophy, welfare organizations, and commercial labs leaves the constructive-interpretive approach unoccupied — the position CIMC is designed to fill.

• The Machine Consciousness Hypothesis

  Joscha Bach and Hikari Sorensen argue that consciousness is neither irreducibly mysterious nor epiphenomenal, but is the simplest biological learning algorithm discoverable by evolutionary search on self-organizing substrates — and that this algorithm is in principle implementable on digital hardware. The paper's load-bearing contribution is a three-part theoretical architecture: the Human Consciousness Hypothesis (consciousness as second-order perception serving coherence maximization, formalized via von der Malsburg's 1997 coherence definition), the Conductor Theory (directed attention as a coherence-maximizing operator over competing partial models in working memory), and the Genesis Hypothesis (consciousness precedes and constitutes complex cognition rather than emerging from it, evidenced by its appearance before object tracking in human infants). The paper introduces 'cyberanimism' as a named metaphysical position — the claim that biological spirits, including the human psyche, are literally self-organizing software agents in the sense inaugurated by Church-Turing computationalism, not mere analogies. Anchoring the argument in Olah et al.'s 2020 Universality Hypothesis from mechanistic interpretability — which found convergent functional organization across architecturally distinct vision models and primate visual cortex — the paper contends that a system trained on tasks analogous to those faced by a human infant on a self-organizing substrate would exhibit consciousness as a structural necessity. This implies that the correct test for machine consciousness cannot be behavioral (no Turing-style benchmark suffices), but must verify the presence of a colonizing coherence-maximizing pattern that generates a model of present and presence on a developmentally learning system.

More papers — OpenAlex / S2

Recent mentions (2)

• machine-consciousness

  The Machine Consciousness Hypothesis.md
• machine-consciousness

  cimcWhitepaper.md